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bycycle-tools / bycycle

Cycle-by-cycle analysis of neural oscillations.
https://bycycle-tools.github.io/
Apache License 2.0
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======================================================== bycycle - cycle-by-cycle analysis of neural oscillations

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ByCycle is a module for analyzing neural oscillations in a cycle-by-cycle approach.

Overview

bycycle is a tool for quantifying features of neural oscillations in the time domain, as opposed to the frequency domain, using a cycle-by-cycle approach. Rather than applying narrowband filters and other methods that use a sinusoidal basis, this approach segments a recording into individual cycles and directly measures each of their properties including amplitude, period, and symmetry.

This is most advantageous for analyzing the waveform shape properties of neural oscillations. It may also provide advantages for studying traditional amplitude and frequency effects, as well. Using cycle properties can also be used for burst detection.

A full description of the method and approach is available in the paper below.

Documentation

Documentation for bycycle is available on the documentation site <https://bycycle-tools.github.io/bycycle/index.html>_.

This documentation includes:

Dependencies

bycycle is written in Python, and requires >= Python 3.6 to run.

It has the following dependencies:

There are also optional dependencies, that offer extra functionality:

Install

The current major release is the 1.X.X series, which is a breaking change from the prior 0.X.X series.

Check the changelog <https://bycycle-tools.github.io/bycycle/changelog.html>_ for notes on updating to the new version.

Stable Version

To install the latest stable release, you can use pip:

.. code-block:: shell

$ pip install bycycle

ByCycle can also be installed with conda, from the conda-forge channel:

.. code-block:: shell

$ conda install -c conda-forge bycycle

Development Version

To get the latest, development version, you can get the code using git:

.. code-block:: shell

$ git clone https://github.com/bycycle-tools/bycycle

To install this cloned copy, move into the directory you just cloned, and run:

.. code-block:: shell

$ pip install .

Editable Version

To install an editable, development version, move into the directory you cloned and install with:

.. code-block:: shell

$ pip install -e .

Reference

If you use this code in your project, please cite:

::

Cole SR & Voytek B (2019) Cycle-by-cycle analysis of neural oscillations. Journal of neurophysiology
122(2), 849-861. DOI: 10.1152/jn.00273.2019

Direct Link: https://doi.org/10.1152/jn.00273.2019

Contribute

This project welcomes and encourages contributions from the community!

To file bug reports and/or ask questions about this project, please use the Github issue tracker <https://github.com/bycycle-tools/bycycle/issues>_.

To see and get involved in discussions about the module, check out:

When interacting with this project, please use the contribution guidelines <https://github.com/bycycle-tools/bycycle/blob/main/CONTRIBUTING.md>_ and follow the code of conduct <https://github.com/bycycle-tools/bycycle/blob/main/CODE_OF_CONDUCT.md>_.

Quickstart

The classes in bycycle are Bycycle, which takes a time series and some parameters as inputs, and returns a table of features for each cycle. BycycleGroup may be used when working with 2d and 3d signals.

For example, consider having a 1-dimensional numpy array, sig, which is a neural signal time series sampled at 1000 Hz (fs) with an alpha (8-12 Hz, f_range) oscillation. We can compute the table of cycle features with the following:

.. code-block:: python

from neurodsp.sim import sim_bursty_oscillation
from bycycle import Bycycle

# Simulate
fs = 1000

f_range = (8, 12)

sig = sim_bursty_oscillation(10, fs, freq=10)

# Fit
bm = Bycycle()

bm.fit(sig, fs, f_range)

bm.df_features

The above example used default parameters to localize extrema and detect bursts of oscillations. However, it is important to knowledgeably select these parameters, as described in the algorithm tutorial <https://bycycle-tools.github.io/bycycle/auto_tutorials/plot_2_bycycle_algorithm.html>_.

The following example introduces some potential parameter changes:

.. code-block:: python

thresholds = {
    'amp_fraction_threshold': .2,
    'amp_consistency_threshold': .5,
    'period_consistency_threshold': .5,
    'monotonicity_threshold': .8,
    'min_n_cycles': 3
}

narrowband_kwargs = {'n_seconds': .5}

bm = Bycycle(
    center_extrema='trough',
    burst_method='cycles',
    thresholds=thresholds,
    find_extrema_kwargs={'filter_kwargs': narrowband_kwargs}
)

bm.fit(sig, fs, f_range)

DataFrame Output


The output of ``bycycle`` is a ``pandas.DataFrame``, which is a table, as shown below.
There are many columns, so the table is split into two images here.

Each row of this table corresponds to an individual segment of the signal, or a putative cycle of
the rhythm of interest.

.. image:: https://github.com/bycycle-tools/bycycle/raw/main/doc/img/cycledf_1.png

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.. image:: https://github.com/bycycle-tools/bycycle/raw/main/doc/img/cycledf_2.png

Columns include:

- **sample_peak**: the sample of the signal at which the peak of this cycle occurs
- **period**: period of the cycle
- **time_peak**: duration of the peak period
- **volt_amp**: amplitude of this cycle, average of the rise and decay voltage
- **time_rdsym**: rise-decay symmetry, the fraction of the cycle in the rise period (0.5 is symmetric)
- **time_ptsym**: peak-trough symmetry, the fraction of the cycle in the peak period (0.5 is symmetric)
- **period_consistency**: consistency between the periods of the adjacent cycles, used in burst detection
- **is_burst**: indicator if the cycle is part of an oscillatory burst

The features in this table can be further analyzed, as demonstrated in the
`resting state data tutorial <https://bycycle-tools.github.io/bycycle/auto_tutorials/plot_2_bycycle_algorithm.html>`_
and the `data example <https://bycycle-tools.github.io/bycycle/auto_examples/plot_1_theta_feature_distributions.html>`_.
For example, we may be interested in the distribution of rise-decay symmetry values in a resting state recording, shown below.

Burst Detection Results

.. image:: https://github.com/bycycle-tools/bycycle/raw/main/doc/img/bursts_detected.png

Funding

Supported by NIH award R01 GM134363 from the NIGMS <https://www.nigms.nih.gov/>_.

.. image:: https://www.nih.gov/sites/all/themes/nih/images/nih-logo-color.png :width: 400

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